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This paper presents a unified system tailored for autonomous pipe segmentation within an industrial setting. To this end, it is designed to analyze RGB images captured by Unmanned Aerial Vehicle (UAV)-mounted cameras to predict binary pipe segmentation maps.

We address distinguishing whether an input is a facial image by learning only a facial-expression recognition (FER) dataset.

We address distinguishing whether an input is a facial image by learning only a facial-expression recognition (FER) dataset.

Generative Adversarial Networks (GANs) have shown promise in augmenting datasets and boosting convolutional neural networks' (CNN) performance on image classification tasks. But they introduce more hyperparameters to tune as well as the need for additional time and computational power to train supplementary to the CNN. In this work, we examine the potential for Auxiliary-Classifier GANs (AC-GANs) as a 'one-stop-shop' architecture for image classification, particularly in low data regimes.

Thermal images reveal medically important physiological information about human stress, signs of inflammation, and emotional mood that cannot be seen on visible images. Providing a method to generate thermal faces from visible images would be highly valuable for the telemedicine community in order to show this medical information. To the best of our knowledge, there are limited works on visible-to-thermal (VT) face translation, and many current works go the opposite direction to generate visible faces from thermal surveillance images (TV) for law enforcement applications.

The automatic diagnosis of lung infections using chest computed
tomography (CT) scans has been recently obtained remarkable significance,
particularly during the COVID-19 pandemic that the early
diagnosis of the disease is of utmost importance. In addition, infection
diagnosis is the main building block of most automated diagnostic/
prognostic frameworks. Recently, due to the devastating effects
of the radiation on the body caused by the CT scan, there has been
a surge in acquiring low and ultra-low-dose CT scans instead of the

Occlusion removal is an interesting application of image enhancement, for which, existing work suggests manually-annotated or domain-specific occlusion removal. No work tries to address automatic occlusion detection and removal as a context-aware generic problem. In this paper, we present a novel methodology to identify objects that do not relate to the image context as occlusions and remove them, reconstructing the space occupied coherently.

This paper proposed a modified YOLOv3 which has an extra object depth prediction module for obstacle detection and avoidance. We use a pre-processed KITTI dataset to train the proposed, unified model for (i) object detection and (ii) depth prediction and use the AirSim flight simulator to generate synthetic aerial images to verify that our model can be applied in different data domains.

Colorization is a challenging task that has recently been tackled by deep learning. Line art colorization is particularly difficult because there is no grayscale value to indicate the color intensities as there is in black-and-white photograph images. When designing a character, concept artists often need to try different color schemes, however, colorization is a time-consuming task. In this article, we propose a semi-automatic framework for colorizing manga concept arts by letting concept artists try different color schemes and obtain colorized results in fashion time.